Papers by Khoa Nguyen
Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics (2026.findings-acl)
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| Challenge: | Recent advances in large language models for test case generation have improved branch coverage via prompt-engineered mutations, limiting their effectiveness for discovering subtle bugs and security vulnerabilities. |
| Approach: | They propose a program structure-aware LLM framework that integrates code property graphs and code semantics to condition test case generation on execution branches. |
| Outcome: | Experiments on real-world projects show that GLMTest improves branch accuracy from 27.4% to 50.2% on TestGenEval benchmark compared with state-of-the-art LLMs, i.e., Claude-Sonnet-4.5 and GPT-4o-mini. |
VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension (2024.eacl-long)
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| Challenge: | Existing datasets for machine reading comprehension tasks in Vietnamese focus on written documents, such as Wikipedia articles, online newspapers, or textbooks. |
| Approach: | They propose to capture Vietnamese spoken language in natural settings and use it to create a machine-learning corpus for machine reading comprehension tasks. |
| Outcome: | The proposed corpus consists of 10,076 question-answer pairs based on 1,230 transcript documents sourced from YouTube . |
Fooling the Textual Fooler via Randomizing Latent Representations (2024.findings-acl)
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| Challenge: | Several adversarial attacks can compromise the model without accessing the model architecture or model parameters (i.e., a blackbox setting) Several studies have revealed that deep NLP models are vulnerable to adversarials that slightly perturb the input to cause the models to misbehave. |
| Approach: | They propose a lightweight and attack-agnostic defense that perplexes the process of generating an adversarial example in query-based black-box attacks. |
| Outcome: | The proposed defense is lightweight and attack-agnostic and does not necessitate additional computational overhead during training nor does it rely on assumptions about the potential adversarial perturbation set while having a negligible impact on the model’s accuracy. |
Z-GMOT: Zero-shot Generic Multiple Object Tracking (2024.findings-naacl)
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Kim Tran, Anh Duy Le Dinh, Tien-Phat Nguyen, Thinh Phan, Pha Nguyen, Khoa Luu, Donald Adjeroh, Gianfranco Doretto, Ngan Le
| Challenge: | Existing approaches to Multi-Object Tracking (MOT) rely on initial bounding boxes and struggle with unseen objects. |
| Approach: | They propose a cutting-edge multi-object tracking solution that can track unseen objects . they propose iGLIP and MA-SORT, which integrate motion and appearance matching strategies . |
| Outcome: | The proposed solution can track objects from never-seen categories without initial bounding boxes or predefined categories. |